Overview

Dataset statistics

Number of variables25
Number of observations460556
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory87.8 MiB
Average record size in memory200.0 B

Variable types

Numeric11
Categorical13
DateTime1

Warnings

lifecycle:transition has constant value "complete" Constant
matricola has constant value "0.0" Constant
org:resource has a high cardinality: 148 distinct values High cardinality
case:concept:name has a high cardinality: 129615 distinct values High cardinality
totalPaymentAmount is highly correlated with paymentAmountHigh correlation
paymentAmount is highly correlated with totalPaymentAmountHigh correlation
timesincemidnight is highly correlated with hourHigh correlation
hour is highly correlated with timesincemidnightHigh correlation
timesincemidnight is highly correlated with hour and 2 other fieldsHigh correlation
dismissal is highly correlated with lifecycle:transition and 1 other fieldsHigh correlation
notificationType is highly correlated with lifecycle:transition and 1 other fieldsHigh correlation
hour is highly correlated with timesincemidnight and 2 other fieldsHigh correlation
article is highly correlated with lifecycle:transition and 1 other fieldsHigh correlation
lifecycle:transition is highly correlated with timesincemidnight and 9 other fieldsHigh correlation
vehicleClass is highly correlated with lifecycle:transition and 1 other fieldsHigh correlation
matricola is highly correlated with timesincemidnight and 9 other fieldsHigh correlation
concept:name is highly correlated with lifecycle:transition and 1 other fieldsHigh correlation
label is highly correlated with lifecycle:transition and 1 other fieldsHigh correlation
lastSent is highly correlated with lifecycle:transition and 1 other fieldsHigh correlation
case:concept:name is uniformly distributed Uniform
totalPaymentAmount has 383320 (83.2%) zeros Zeros
points has 445503 (96.7%) zeros Zeros
expense has 186095 (40.4%) zeros Zeros
paymentAmount has 383321 (83.2%) zeros Zeros
weekday has 73350 (15.9%) zeros Zeros
timesincelastevent has 141493 (30.7%) zeros Zeros
timesincecasestart has 139927 (30.4%) zeros Zeros

Reproduction

Analysis started2021-03-23 07:54:10.611623
Analysis finished2021-03-23 07:55:59.782810
Duration1 minute and 49.17 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

amount
Real number (ℝ≥0)

Distinct204
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.27446406
Minimum0
Maximum8000
Zeros111
Zeros (%)< 0.1%
Memory size3.5 MiB
2021-03-23T08:55:59.836685image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile23
Q133.6
median36
Q368.77
95-th percentile143
Maximum8000
Range8000
Interquartile range (IQR)35.17

Descriptive statistics

Standard deviation83.15059319
Coefficient of variation (CV)1.402806327
Kurtosis1085.624752
Mean59.27446406
Median Absolute Deviation (MAD)4
Skewness19.66363976
Sum27299210.07
Variance6914.021148
MonotocityNot monotonic
2021-03-23T08:55:59.944888image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3556158
 
12.2%
33.641682
 
9.1%
3640830
 
8.9%
3831870
 
6.9%
31.330434
 
6.6%
32.823687
 
5.1%
3220126
 
4.4%
3918424
 
4.0%
71.516704
 
3.6%
7415892
 
3.5%
Other values (194)164749
35.8%
ValueCountFrequency (%)
0111
 
< 0.1%
18.783811
0.8%
191570
0.3%
19.682988
0.6%
19.952454
0.5%
ValueCountFrequency (%)
80003
< 0.1%
75462
 
< 0.1%
43513
< 0.1%
40004
< 0.1%
3684.57
< 0.1%

org:resource
Categorical

HIGH CARDINALITY

Distinct148
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
538.0
 
27429
550.0
 
24515
541.0
 
21934
537.0
 
21175
559.0
 
17749
Other values (143)
347754 

Length

Max length5
Median length5
Mean length4.655681394
Min length3

Characters and Unicode

Total characters2144202
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row561.0
2nd row561.0
3rd row561.0
4th row561.0
5th row561.0
ValueCountFrequency (%)
538.027429
 
6.0%
550.024515
 
5.3%
541.021934
 
4.8%
537.021175
 
4.6%
559.017749
 
3.9%
536.015423
 
3.3%
557.015152
 
3.3%
49.013710
 
3.0%
561.010980
 
2.4%
558.010677
 
2.3%
Other values (138)281812
61.2%
2021-03-23T08:56:00.177026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
538.027429
 
6.0%
550.024515
 
5.3%
541.021934
 
4.8%
537.021175
 
4.6%
559.017749
 
3.9%
536.015423
 
3.3%
557.015152
 
3.3%
49.013710
 
3.0%
561.010980
 
2.4%
558.010677
 
2.3%
Other values (138)281812
61.2%

Most occurring characters

ValueCountFrequency (%)
0536822
25.0%
.460547
21.5%
5361448
16.9%
8162446
 
7.6%
3134524
 
6.3%
4115253
 
5.4%
691436
 
4.3%
183819
 
3.9%
277651
 
3.6%
760630
 
2.8%
Other values (6)59626
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1683610
78.5%
Other Punctuation460547
 
21.5%
Lowercase Letter45
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
0536822
31.9%
5361448
21.5%
8162446
 
9.6%
3134524
 
8.0%
4115253
 
6.8%
691436
 
5.4%
183819
 
5.0%
277651
 
4.6%
760630
 
3.6%
959581
 
3.5%
ValueCountFrequency (%)
o9
20.0%
t9
20.0%
h9
20.0%
e9
20.0%
r9
20.0%
ValueCountFrequency (%)
.460547
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2144157
> 99.9%
Latin45
 
< 0.1%

Most frequent character per script

ValueCountFrequency (%)
0536822
25.0%
.460547
21.5%
5361448
16.9%
8162446
 
7.6%
3134524
 
6.3%
4115253
 
5.4%
691436
 
4.3%
183819
 
3.9%
277651
 
3.6%
760630
 
2.8%
ValueCountFrequency (%)
o9
20.0%
t9
20.0%
h9
20.0%
e9
20.0%
r9
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2144202
100.0%

Most frequent character per block

ValueCountFrequency (%)
0536822
25.0%
.460547
21.5%
5361448
16.9%
8162446
 
7.6%
3134524
 
6.3%
4115253
 
5.4%
691436
 
4.3%
183819
 
3.9%
277651
 
3.6%
760630
 
2.8%
Other values (6)59626
 
2.8%

dismissal
Categorical

HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
NIL
458332 
#
 
2030
G
 
105
@
 
37
D
 
17
Other values (3)
 
35

Length

Max length5
Median length3
Mean length2.99044633
Min length1

Characters and Unicode

Total characters1377268
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNIL
2nd rowNIL
3rd rowNIL
4th rowNIL
5th rowNIL
ValueCountFrequency (%)
NIL458332
99.5%
#2030
 
0.4%
G105
 
< 0.1%
@37
 
< 0.1%
D17
 
< 0.1%
other12
 
< 0.1%
C12
 
< 0.1%
411
 
< 0.1%
2021-03-23T08:56:00.357352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:56:00.426246image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
nil458332
99.5%
2067
 
0.4%
g105
 
< 0.1%
d17
 
< 0.1%
other12
 
< 0.1%
c12
 
< 0.1%
411
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N458332
33.3%
I458332
33.3%
L458332
33.3%
#2030
 
0.1%
G105
 
< 0.1%
@37
 
< 0.1%
D17
 
< 0.1%
C12
 
< 0.1%
o12
 
< 0.1%
t12
 
< 0.1%
Other values (4)47
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1375130
99.8%
Other Punctuation2067
 
0.2%
Lowercase Letter60
 
< 0.1%
Decimal Number11
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
N458332
33.3%
I458332
33.3%
L458332
33.3%
G105
 
< 0.1%
D17
 
< 0.1%
C12
 
< 0.1%
ValueCountFrequency (%)
o12
20.0%
t12
20.0%
h12
20.0%
e12
20.0%
r12
20.0%
ValueCountFrequency (%)
#2030
98.2%
@37
 
1.8%
ValueCountFrequency (%)
411
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1375190
99.8%
Common2078
 
0.2%

Most frequent character per script

ValueCountFrequency (%)
N458332
33.3%
I458332
33.3%
L458332
33.3%
G105
 
< 0.1%
D17
 
< 0.1%
C12
 
< 0.1%
o12
 
< 0.1%
t12
 
< 0.1%
h12
 
< 0.1%
e12
 
< 0.1%
ValueCountFrequency (%)
#2030
97.7%
@37
 
1.8%
411
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1377268
100.0%

Most frequent character per block

ValueCountFrequency (%)
N458332
33.3%
I458332
33.3%
L458332
33.3%
#2030
 
0.1%
G105
 
< 0.1%
@37
 
< 0.1%
D17
 
< 0.1%
C12
 
< 0.1%
o12
 
< 0.1%
t12
 
< 0.1%
Other values (4)47
 
< 0.1%

concept:name
Categorical

HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
Create Fine
129615 
Send Fine
83232 
Insert Fine Notification
79860 
Add penalty
79860 
Payment
77237 
Other values (5)
 
10752

Length

Max length37
Median length11
Mean length12.6400264
Min length7

Characters and Unicode

Total characters5821440
Distinct characters30
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCreate Fine
2nd rowSend Fine
3rd rowInsert Fine Notification
4th rowAdd penalty
5th rowCreate Fine
ValueCountFrequency (%)
Create Fine129615
28.1%
Send Fine83232
18.1%
Insert Fine Notification79860
17.3%
Add penalty79860
17.3%
Payment77237
16.8%
Insert Date Appeal to Prefecture4184
 
0.9%
Send Appeal to Prefecture4121
 
0.9%
Receive Result Appeal from Prefecture999
 
0.2%
Notify Result Appeal to Offender896
 
0.2%
Appeal to Judge552
 
0.1%
2021-03-23T08:56:00.614656image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:56:00.685264image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
fine292707
30.8%
create129615
13.6%
send87353
 
9.2%
insert84044
 
8.8%
notification79860
 
8.4%
penalty79860
 
8.4%
add79860
 
8.4%
payment77237
 
8.1%
appeal10752
 
1.1%
to9753
 
1.0%
Other values (8)19725
 
2.1%

Most occurring characters

ValueCountFrequency (%)
e930515
16.0%
n701957
12.1%
t556508
9.6%
i534182
9.2%
490210
 
8.4%
a381508
 
6.6%
F292707
 
5.0%
d248521
 
4.3%
r234162
 
4.0%
o171368
 
2.9%
Other values (20)1279802
22.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4471076
76.8%
Uppercase Letter860154
 
14.8%
Space Separator490210
 
8.4%

Most frequent character per category

ValueCountFrequency (%)
e930515
20.8%
n701957
15.7%
t556508
12.4%
i534182
11.9%
a381508
8.5%
d248521
 
5.6%
r234162
 
5.2%
o171368
 
3.8%
y157993
 
3.5%
p101364
 
2.3%
Other values (8)452998
10.1%
ValueCountFrequency (%)
F292707
34.0%
C129615
15.1%
A90612
 
10.5%
S87353
 
10.2%
P86541
 
10.1%
I84044
 
9.8%
N80756
 
9.4%
D4184
 
0.5%
R2894
 
0.3%
O896
 
0.1%
ValueCountFrequency (%)
490210
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5331230
91.6%
Common490210
 
8.4%

Most frequent character per script

ValueCountFrequency (%)
e930515
17.5%
n701957
13.2%
t556508
10.4%
i534182
10.0%
a381508
 
7.2%
F292707
 
5.5%
d248521
 
4.7%
r234162
 
4.4%
o171368
 
3.2%
y157993
 
3.0%
Other values (19)1121809
21.0%
ValueCountFrequency (%)
490210
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5821440
100.0%

Most frequent character per block

ValueCountFrequency (%)
e930515
16.0%
n701957
12.1%
t556508
9.6%
i534182
9.2%
490210
 
8.4%
a381508
 
6.6%
F292707
 
5.0%
d248521
 
4.3%
r234162
 
4.0%
o171368
 
2.9%
Other values (20)1279802
22.0%

vehicleClass
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
A
447633 
C
 
8524
M
 
4391
R
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters460556
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA
ValueCountFrequency (%)
A447633
97.2%
C8524
 
1.9%
M4391
 
1.0%
R8
 
< 0.1%
2021-03-23T08:56:00.881785image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:56:00.935533image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
a447633
97.2%
c8524
 
1.9%
m4391
 
1.0%
r8
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A447633
97.2%
C8524
 
1.9%
M4391
 
1.0%
R8
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter460556
100.0%

Most frequent character per category

ValueCountFrequency (%)
A447633
97.2%
C8524
 
1.9%
M4391
 
1.0%
R8
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin460556
100.0%

Most frequent character per script

ValueCountFrequency (%)
A447633
97.2%
C8524
 
1.9%
M4391
 
1.0%
R8
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII460556
100.0%

Most frequent character per block

ValueCountFrequency (%)
A447633
97.2%
C8524
 
1.9%
M4391
 
1.0%
R8
 
< 0.1%

totalPaymentAmount
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1006
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.789659347
Minimum0
Maximum4021
Zeros383320
Zeros (%)83.2%
Memory size3.5 MiB
2021-03-23T08:56:01.017652image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile46
Maximum4021
Range4021
Interquartile range (IQR)0

Descriptive statistics

Standard deviation27.35406785
Coefficient of variation (CV)3.112073719
Kurtosis1234.297158
Mean8.789659347
Median Absolute Deviation (MAD)0
Skewness15.68362497
Sum4048130.35
Variance748.2450282
MonotocityNot monotonic
2021-03-23T08:56:01.126365image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0383320
83.2%
3510693
 
2.3%
367200
 
1.6%
33.66341
 
1.4%
385772
 
1.3%
394146
 
0.9%
31.32949
 
0.6%
32.82449
 
0.5%
322294
 
0.5%
461680
 
0.4%
Other values (996)33712
 
7.3%
ValueCountFrequency (%)
0383320
83.2%
1.321
 
< 0.1%
1.541
 
< 0.1%
3.282
 
< 0.1%
3.361
 
< 0.1%
ValueCountFrequency (%)
40211
< 0.1%
18421
< 0.1%
15971
< 0.1%
15201
< 0.1%
1498.51
< 0.1%

lifecycle:transition
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
complete
460556 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters3684448
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcomplete
2nd rowcomplete
3rd rowcomplete
4th rowcomplete
5th rowcomplete
ValueCountFrequency (%)
complete460556
100.0%
2021-03-23T08:56:01.306056image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:56:01.356929image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
complete460556
100.0%

Most occurring characters

ValueCountFrequency (%)
e921112
25.0%
c460556
12.5%
o460556
12.5%
m460556
12.5%
p460556
12.5%
l460556
12.5%
t460556
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3684448
100.0%

Most frequent character per category

ValueCountFrequency (%)
e921112
25.0%
c460556
12.5%
o460556
12.5%
m460556
12.5%
p460556
12.5%
l460556
12.5%
t460556
12.5%

Most occurring scripts

ValueCountFrequency (%)
Latin3684448
100.0%

Most frequent character per script

ValueCountFrequency (%)
e921112
25.0%
c460556
12.5%
o460556
12.5%
m460556
12.5%
p460556
12.5%
l460556
12.5%
t460556
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII3684448
100.0%

Most frequent character per block

ValueCountFrequency (%)
e921112
25.0%
c460556
12.5%
o460556
12.5%
m460556
12.5%
p460556
12.5%
l460556
12.5%
t460556
12.5%
Distinct4903
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
Minimum1999-12-31 23:00:00+00:00
Maximum2013-06-17 22:00:00+00:00
2021-03-23T08:56:01.419978image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:56:01.524868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

article
Categorical

HIGH CORRELATION

Distinct50
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
157.0
200873 
7.0
127905 
158.0
84174 
142.0
23250 
180.0
 
4038
Other values (45)
20316 

Length

Max length5
Median length5
Mean length4.429891262
Min length3

Characters and Unicode

Total characters2040213
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row157.0
2nd row157.0
3rd row157.0
4th row157.0
5th row157.0
ValueCountFrequency (%)
157.0200873
43.6%
7.0127905
27.8%
158.084174
18.3%
142.023250
 
5.0%
180.04038
 
0.9%
181.03503
 
0.8%
80.02378
 
0.5%
171.02326
 
0.5%
172.01781
 
0.4%
146.01348
 
0.3%
Other values (40)8980
 
1.9%
2021-03-23T08:56:01.757763image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
157.0200873
43.6%
7.0127905
27.8%
158.084174
18.3%
142.023250
 
5.0%
180.04038
 
0.9%
181.03503
 
0.8%
80.02378
 
0.5%
171.02326
 
0.5%
172.01781
 
0.4%
146.01348
 
0.3%
Other values (40)8980
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0468296
23.0%
.460493
22.6%
1333779
16.4%
7333721
16.4%
5285898
14.0%
894483
 
4.6%
428427
 
1.4%
227139
 
1.3%
33701
 
0.2%
92163
 
0.1%
Other values (6)2113
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1579405
77.4%
Other Punctuation460493
 
22.6%
Lowercase Letter315
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
0468296
29.7%
1333779
21.1%
7333721
21.1%
5285898
18.1%
894483
 
6.0%
428427
 
1.8%
227139
 
1.7%
33701
 
0.2%
92163
 
0.1%
61798
 
0.1%
ValueCountFrequency (%)
o63
20.0%
t63
20.0%
h63
20.0%
e63
20.0%
r63
20.0%
ValueCountFrequency (%)
.460493
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2039898
> 99.9%
Latin315
 
< 0.1%

Most frequent character per script

ValueCountFrequency (%)
0468296
23.0%
.460493
22.6%
1333779
16.4%
7333721
16.4%
5285898
14.0%
894483
 
4.6%
428427
 
1.4%
227139
 
1.3%
33701
 
0.2%
92163
 
0.1%
ValueCountFrequency (%)
o63
20.0%
t63
20.0%
h63
20.0%
e63
20.0%
r63
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2040213
100.0%

Most frequent character per block

ValueCountFrequency (%)
0468296
23.0%
.460493
22.6%
1333779
16.4%
7333721
16.4%
5285898
14.0%
894483
 
4.6%
428427
 
1.4%
227139
 
1.3%
33701
 
0.2%
92163
 
0.1%
Other values (6)2113
 
0.1%

points
Real number (ℝ≥0)

ZEROS

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1104773361
Minimum0
Maximum10
Zeros445503
Zeros (%)96.7%
Memory size3.5 MiB
2021-03-23T08:56:01.842830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.68429259
Coefficient of variation (CV)6.193963524
Kurtosis66.47490183
Mean0.1104773361
Median Absolute Deviation (MAD)0
Skewness7.569668316
Sum50881
Variance0.4682563487
MonotocityNot monotonic
2021-03-23T08:56:01.917044image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0445503
96.7%
28553
 
1.9%
54627
 
1.0%
6798
 
0.2%
3606
 
0.1%
10300
 
0.1%
897
 
< 0.1%
462
 
< 0.1%
110
 
< 0.1%
ValueCountFrequency (%)
0445503
96.7%
110
 
< 0.1%
28553
 
1.9%
3606
 
0.1%
462
 
< 0.1%
ValueCountFrequency (%)
10300
 
0.1%
897
 
< 0.1%
6798
 
0.2%
54627
1.0%
462
 
< 0.1%

case:concept:name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct129615
Distinct (%)28.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
C20817
 
20
C17607
 
17
V8169
 
17
C10852
 
16
C19108
 
16
Other values (129610)
460470 

Length

Max length7
Median length6
Mean length6.197980267
Min length2

Characters and Unicode

Total characters2854517
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA100
2nd rowA100
3rd rowA100
4th rowA100
5th rowA10000
ValueCountFrequency (%)
C2081720
 
< 0.1%
C1760717
 
< 0.1%
V816917
 
< 0.1%
C1085216
 
< 0.1%
C1910816
 
< 0.1%
V957615
 
< 0.1%
C1306215
 
< 0.1%
C1819015
 
< 0.1%
V1124914
 
< 0.1%
V1885314
 
< 0.1%
Other values (129605)460397
> 99.9%
2021-03-23T08:56:02.350492image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
c2081720
 
< 0.1%
v816917
 
< 0.1%
c1760717
 
< 0.1%
c1085216
 
< 0.1%
c1910816
 
< 0.1%
c1306215
 
< 0.1%
v957615
 
< 0.1%
c1819015
 
< 0.1%
v1885314
 
< 0.1%
v1124914
 
< 0.1%
Other values (129605)460397
> 99.9%

Most occurring characters

ValueCountFrequency (%)
1365782
12.8%
2242301
8.5%
6234259
8.2%
4234136
8.2%
5232659
8.2%
3232328
8.1%
7228071
8.0%
8217977
7.6%
S209594
7.3%
9209537
7.3%
Other values (7)447873
15.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2393961
83.9%
Uppercase Letter460556
 
16.1%

Most frequent character per category

ValueCountFrequency (%)
1365782
15.3%
2242301
10.1%
6234259
9.8%
4234136
9.8%
5232659
9.7%
3232328
9.7%
7228071
9.5%
8217977
9.1%
9209537
8.8%
0196911
8.2%
ValueCountFrequency (%)
S209594
45.5%
N139455
30.3%
A64076
 
13.9%
V23183
 
5.0%
C16823
 
3.7%
P7328
 
1.6%
L97
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common2393961
83.9%
Latin460556
 
16.1%

Most frequent character per script

ValueCountFrequency (%)
1365782
15.3%
2242301
10.1%
6234259
9.8%
4234136
9.8%
5232659
9.7%
3232328
9.7%
7228071
9.5%
8217977
9.1%
9209537
8.8%
0196911
8.2%
ValueCountFrequency (%)
S209594
45.5%
N139455
30.3%
A64076
 
13.9%
V23183
 
5.0%
C16823
 
3.7%
P7328
 
1.6%
L97
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2854517
100.0%

Most frequent character per block

ValueCountFrequency (%)
1365782
12.8%
2242301
8.5%
6234259
8.2%
4234136
8.2%
5232659
8.2%
3232328
8.1%
7228071
8.0%
8217977
7.6%
S209594
7.3%
9209537
7.3%
Other values (7)447873
15.7%

expense
Real number (ℝ≥0)

ZEROS

Distinct84
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.415624962
Minimum0
Maximum60
Zeros186095
Zeros (%)40.4%
Memory size3.5 MiB
2021-03-23T08:56:02.459064image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median10
Q313.5
95-th percentile16.6
Maximum60
Range60
Interquartile range (IQR)13.5

Descriptive statistics

Standard deviation6.714056052
Coefficient of variation (CV)0.9053931511
Kurtosis-0.8927615652
Mean7.415624962
Median Absolute Deviation (MAD)5
Skewness0.1981031616
Sum3415310.57
Variance45.07854867
MonotocityNot monotonic
2021-03-23T08:56:02.565963image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0186095
40.4%
13.547123
 
10.2%
1143381
 
9.4%
1023281
 
5.1%
1322892
 
5.0%
6.7122858
 
5.0%
1520225
 
4.4%
14.2515411
 
3.3%
6.4615109
 
3.3%
15.1612473
 
2.7%
Other values (74)51708
 
11.2%
ValueCountFrequency (%)
0186095
40.4%
4.9615
 
< 0.1%
5.618
 
< 0.1%
5.883
 
< 0.1%
6.4615109
 
3.3%
ValueCountFrequency (%)
603
 
< 0.1%
463
 
< 0.1%
4551
< 0.1%
4014
 
< 0.1%
35124
< 0.1%

notificationType
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
missing
263132 
P
196977 
C
 
447

Length

Max length7
Median length7
Mean length4.428013097
Min length1

Characters and Unicode

Total characters2039348
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmissing
2nd rowmissing
3rd rowP
4th rowP
5th rowmissing
ValueCountFrequency (%)
missing263132
57.1%
P196977
42.8%
C447
 
0.1%
2021-03-23T08:56:02.758823image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:56:02.817465image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
missing263132
57.1%
p196977
42.8%
c447
 
0.1%

Most occurring characters

ValueCountFrequency (%)
i526264
25.8%
s526264
25.8%
m263132
12.9%
n263132
12.9%
g263132
12.9%
P196977
 
9.7%
C447
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1841924
90.3%
Uppercase Letter197424
 
9.7%

Most frequent character per category

ValueCountFrequency (%)
i526264
28.6%
s526264
28.6%
m263132
14.3%
n263132
14.3%
g263132
14.3%
ValueCountFrequency (%)
P196977
99.8%
C447
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin2039348
100.0%

Most frequent character per script

ValueCountFrequency (%)
i526264
25.8%
s526264
25.8%
m263132
12.9%
n263132
12.9%
g263132
12.9%
P196977
 
9.7%
C447
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2039348
100.0%

Most frequent character per block

ValueCountFrequency (%)
i526264
25.8%
s526264
25.8%
m263132
12.9%
n263132
12.9%
g263132
12.9%
P196977
 
9.7%
C447
 
< 0.1%

lastSent
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
missing
267269 
P
117862 
N
71767 
C
 
3658

Length

Max length7
Median length7
Mean length4.481908823
Min length1

Characters and Unicode

Total characters2064170
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmissing
2nd rowmissing
3rd rowP
4th rowP
5th rowmissing
ValueCountFrequency (%)
missing267269
58.0%
P117862
25.6%
N71767
 
15.6%
C3658
 
0.8%
2021-03-23T08:56:02.976466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:56:03.036165image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
missing267269
58.0%
p117862
25.6%
n71767
 
15.6%
c3658
 
0.8%

Most occurring characters

ValueCountFrequency (%)
i534538
25.9%
s534538
25.9%
m267269
12.9%
n267269
12.9%
g267269
12.9%
P117862
 
5.7%
N71767
 
3.5%
C3658
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1870883
90.6%
Uppercase Letter193287
 
9.4%

Most frequent character per category

ValueCountFrequency (%)
i534538
28.6%
s534538
28.6%
m267269
14.3%
n267269
14.3%
g267269
14.3%
ValueCountFrequency (%)
P117862
61.0%
N71767
37.1%
C3658
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Latin2064170
100.0%

Most frequent character per script

ValueCountFrequency (%)
i534538
25.9%
s534538
25.9%
m267269
12.9%
n267269
12.9%
g267269
12.9%
P117862
 
5.7%
N71767
 
3.5%
C3658
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII2064170
100.0%

Most frequent character per block

ValueCountFrequency (%)
i534538
25.9%
s534538
25.9%
m267269
12.9%
n267269
12.9%
g267269
12.9%
P117862
 
5.7%
N71767
 
3.5%
C3658
 
0.2%

paymentAmount
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1000
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.790931809
Minimum0
Maximum3975
Zeros383321
Zeros (%)83.2%
Memory size3.5 MiB
2021-03-23T08:56:03.120105image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile39.51
Maximum3975
Range3975
Interquartile range (IQR)0

Descriptive statistics

Standard deviation23.70380841
Coefficient of variation (CV)3.042486956
Kurtosis1915.539696
Mean7.790931809
Median Absolute Deviation (MAD)0
Skewness18.10141562
Sum3588160.39
Variance561.8705329
MonotocityNot monotonic
2021-03-23T08:56:03.229755image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0383321
83.2%
3510705
 
2.3%
367204
 
1.6%
33.66344
 
1.4%
386197
 
1.3%
394149
 
0.9%
31.33148
 
0.7%
32.82607
 
0.6%
322293
 
0.5%
461482
 
0.3%
Other values (990)33106
 
7.2%
ValueCountFrequency (%)
0383321
83.2%
0.21
 
< 0.1%
0.4418
 
< 0.1%
0.452
 
< 0.1%
0.51
 
< 0.1%
ValueCountFrequency (%)
39751
< 0.1%
15971
< 0.1%
1498.51
< 0.1%
1433.51
< 0.1%
10641
< 0.1%

matricola
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
0.0
460556 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1381668
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0460556
100.0%
2021-03-23T08:56:03.409967image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:56:03.460963image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0460556
100.0%

Most occurring characters

ValueCountFrequency (%)
0921112
66.7%
.460556
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number921112
66.7%
Other Punctuation460556
33.3%

Most frequent character per category

ValueCountFrequency (%)
0921112
100.0%
ValueCountFrequency (%)
.460556
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1381668
100.0%

Most frequent character per script

ValueCountFrequency (%)
0921112
66.7%
.460556
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1381668
100.0%

Most frequent character per block

ValueCountFrequency (%)
0921112
66.7%
.460556
33.3%

timesincemidnight
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
1320.0
277629 
1380.0
182927 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters2763336
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1320.0
2nd row1380.0
3rd row1380.0
4th row1380.0
5th row1380.0
ValueCountFrequency (%)
1320.0277629
60.3%
1380.0182927
39.7%
2021-03-23T08:56:03.591494image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:56:03.643600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1320.0277629
60.3%
1380.0182927
39.7%

Most occurring characters

ValueCountFrequency (%)
0921112
33.3%
1460556
16.7%
3460556
16.7%
.460556
16.7%
2277629
 
10.0%
8182927
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2302780
83.3%
Other Punctuation460556
 
16.7%

Most frequent character per category

ValueCountFrequency (%)
0921112
40.0%
1460556
20.0%
3460556
20.0%
2277629
 
12.1%
8182927
 
7.9%
ValueCountFrequency (%)
.460556
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2763336
100.0%

Most frequent character per script

ValueCountFrequency (%)
0921112
33.3%
1460556
16.7%
3460556
16.7%
.460556
16.7%
2277629
 
10.0%
8182927
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII2763336
100.0%

Most frequent character per block

ValueCountFrequency (%)
0921112
33.3%
1460556
16.7%
3460556
16.7%
.460556
16.7%
2277629
 
10.0%
8182927
 
6.6%

month
Real number (ℝ≥0)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.028011794
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Memory size3.5 MiB
2021-03-23T08:56:03.698501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.363824047
Coefficient of variation (CV)0.4786309622
Kurtosis-1.025808663
Mean7.028011794
Median Absolute Deviation (MAD)3
Skewness-0.2539622477
Sum3236793
Variance11.31531222
MonotocityNot monotonic
2021-03-23T08:56:03.772941image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
752306
11.4%
852264
11.3%
1146759
10.2%
944758
9.7%
1244203
9.6%
637634
8.2%
1034778
7.6%
134383
7.5%
530283
6.6%
229254
6.4%
Other values (2)53934
11.7%
ValueCountFrequency (%)
134383
7.5%
229254
6.4%
328694
6.2%
425240
5.5%
530283
6.6%
ValueCountFrequency (%)
1244203
9.6%
1146759
10.2%
1034778
7.6%
944758
9.7%
852264
11.3%

weekday
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.876770686
Minimum0
Maximum6
Zeros73350
Zeros (%)15.9%
Memory size3.5 MiB
2021-03-23T08:56:03.847289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.01769928
Coefficient of variation (CV)0.701376474
Kurtosis-1.187585179
Mean2.876770686
Median Absolute Deviation (MAD)2
Skewness0.127768362
Sum1324914
Variance4.071110385
MonotocityNot monotonic
2021-03-23T08:56:03.919574image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
377800
16.9%
673860
16.0%
073350
15.9%
167433
14.6%
265637
14.3%
462733
13.6%
539743
8.6%
ValueCountFrequency (%)
073350
15.9%
167433
14.6%
265637
14.3%
377800
16.9%
462733
13.6%
ValueCountFrequency (%)
673860
16.0%
539743
8.6%
462733
13.6%
377800
16.9%
265637
14.3%

hour
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
22.0
277629 
23.0
182927 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1842224
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row22.0
2nd row23.0
3rd row23.0
4th row23.0
5th row23.0
ValueCountFrequency (%)
22.0277629
60.3%
23.0182927
39.7%
2021-03-23T08:56:04.085686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:56:04.137608image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
22.0277629
60.3%
23.0182927
39.7%

Most occurring characters

ValueCountFrequency (%)
2738185
40.1%
.460556
25.0%
0460556
25.0%
3182927
 
9.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1381668
75.0%
Other Punctuation460556
 
25.0%

Most frequent character per category

ValueCountFrequency (%)
2738185
53.4%
0460556
33.3%
3182927
 
13.2%
ValueCountFrequency (%)
.460556
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1842224
100.0%

Most frequent character per script

ValueCountFrequency (%)
2738185
40.1%
.460556
25.0%
0460556
25.0%
3182927
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1842224
100.0%

Most frequent character per block

ValueCountFrequency (%)
2738185
40.1%
.460556
25.0%
0460556
25.0%
3182927
 
9.9%

timesincelastevent
Real number (ℝ≥0)

ZEROS

Distinct2115
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57343.91727
Minimum0
Maximum6078300
Zeros141493
Zeros (%)30.7%
Memory size3.5 MiB
2021-03-23T08:56:04.208961image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median24420
Q386400
95-th percentile187140
Maximum6078300
Range6078300
Interquartile range (IQR)86400

Descriptive statistics

Standard deviation118377.8676
Coefficient of variation (CV)2.064349164
Kurtosis423.534513
Mean57343.91727
Median Absolute Deviation (MAD)24420
Skewness14.62238983
Sum2.641008516 × 1010
Variance1.401331955 × 1010
MonotocityNot monotonic
2021-03-23T08:56:04.319209image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0141493
30.7%
8640051301
 
11.1%
8646016300
 
3.5%
100808156
 
1.8%
86408109
 
1.8%
14406888
 
1.5%
72006458
 
1.4%
115206253
 
1.4%
863406016
 
1.3%
129605599
 
1.2%
Other values (2105)203983
44.3%
ValueCountFrequency (%)
0141493
30.7%
138022
 
< 0.1%
14406888
 
1.5%
150018
 
< 0.1%
282026
 
< 0.1%
ValueCountFrequency (%)
60783001
< 0.1%
60768601
< 0.1%
60610202
< 0.1%
59127001
< 0.1%
58636801
< 0.1%

timesincecasestart
Real number (ℝ≥0)

ZEROS

Distinct2793
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean125971.0222
Minimum0
Maximum6295680
Zeros139927
Zeros (%)30.4%
Memory size3.5 MiB
2021-03-23T08:56:04.436455image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median99360
Q3197340
95-th percentile316860
Maximum6295680
Range6295680
Interquartile range (IQR)197340

Descriptive statistics

Standard deviation168723.8716
Coefficient of variation (CV)1.339386382
Kurtosis130.2035847
Mean125971.0222
Median Absolute Deviation (MAD)99360
Skewness6.922687921
Sum5.801671008 × 1010
Variance2.846774486 × 1010
MonotocityNot monotonic
2021-03-23T08:56:04.542110image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0139927
30.4%
14405530
 
1.2%
28804445
 
1.0%
43204219
 
0.9%
57603875
 
0.8%
72003781
 
0.8%
86403654
 
0.8%
864003199
 
0.7%
100802838
 
0.6%
1987801630
 
0.4%
Other values (2783)287458
62.4%
ValueCountFrequency (%)
0139927
30.4%
138016
 
< 0.1%
14405530
 
1.2%
150014
 
< 0.1%
282019
 
< 0.1%
ValueCountFrequency (%)
62956801
< 0.1%
62668801
< 0.1%
62510401
< 0.1%
62481601
< 0.1%
62208001
< 0.1%

event_nr
Real number (ℝ≥0)

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.539135306
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Memory size3.5 MiB
2021-03-23T08:56:04.639859image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile5
Maximum20
Range19
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.403700132
Coefficient of variation (CV)0.5528260461
Kurtosis1.28970217
Mean2.539135306
Median Absolute Deviation (MAD)1
Skewness0.9225426208
Sum1169414
Variance1.97037406
MonotocityNot monotonic
2021-03-23T08:56:04.722648image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1129615
28.1%
2129615
28.1%
383244
18.1%
480094
17.4%
523391
 
5.1%
611606
 
2.5%
71265
 
0.3%
8983
 
0.2%
9560
 
0.1%
1089
 
< 0.1%
Other values (10)94
 
< 0.1%
ValueCountFrequency (%)
1129615
28.1%
2129615
28.1%
383244
18.1%
480094
17.4%
523391
 
5.1%
ValueCountFrequency (%)
201
 
< 0.1%
191
 
< 0.1%
181
 
< 0.1%
173
< 0.1%
165
< 0.1%

open_cases
Real number (ℝ≥0)

Distinct3649
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10662.84286
Minimum0
Maximum17269
Zeros16
Zeros (%)< 0.1%
Memory size3.5 MiB
2021-03-23T08:56:04.829088image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5898
Q18987
median10723
Q312407
95-th percentile15581
Maximum17269
Range17269
Interquartile range (IQR)3420

Descriptive statistics

Standard deviation2924.895276
Coefficient of variation (CV)0.2743072664
Kurtosis0.7933714713
Mean10662.84286
Median Absolute Deviation (MAD)1719
Skewness-0.4422451051
Sum4910836254
Variance8555012.374
MonotocityNot monotonic
2021-03-23T08:56:04.933614image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
164921808
 
0.4%
121051554
 
0.3%
75091367
 
0.3%
104481319
 
0.3%
103561304
 
0.3%
123081296
 
0.3%
103521255
 
0.3%
117541136
 
0.2%
87591106
 
0.2%
128461103
 
0.2%
Other values (3639)447308
97.1%
ValueCountFrequency (%)
016
< 0.1%
11
 
< 0.1%
54
 
< 0.1%
1415
< 0.1%
1523
< 0.1%
ValueCountFrequency (%)
1726926
< 0.1%
1726837
< 0.1%
1726616
 
< 0.1%
1726431
< 0.1%
1726163
< 0.1%

label
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
deviant
239478 
regular
221078 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters3223892
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdeviant
2nd rowdeviant
3rd rowdeviant
4th rowdeviant
5th rowregular
ValueCountFrequency (%)
deviant239478
52.0%
regular221078
48.0%
2021-03-23T08:56:05.122297image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:56:05.174889image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
deviant239478
52.0%
regular221078
48.0%

Most occurring characters

ValueCountFrequency (%)
e460556
14.3%
a460556
14.3%
r442156
13.7%
d239478
7.4%
v239478
7.4%
i239478
7.4%
n239478
7.4%
t239478
7.4%
g221078
6.9%
u221078
6.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3223892
100.0%

Most frequent character per category

ValueCountFrequency (%)
e460556
14.3%
a460556
14.3%
r442156
13.7%
d239478
7.4%
v239478
7.4%
i239478
7.4%
n239478
7.4%
t239478
7.4%
g221078
6.9%
u221078
6.9%

Most occurring scripts

ValueCountFrequency (%)
Latin3223892
100.0%

Most frequent character per script

ValueCountFrequency (%)
e460556
14.3%
a460556
14.3%
r442156
13.7%
d239478
7.4%
v239478
7.4%
i239478
7.4%
n239478
7.4%
t239478
7.4%
g221078
6.9%
u221078
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII3223892
100.0%

Most frequent character per block

ValueCountFrequency (%)
e460556
14.3%
a460556
14.3%
r442156
13.7%
d239478
7.4%
v239478
7.4%
i239478
7.4%
n239478
7.4%
t239478
7.4%
g221078
6.9%
u221078
6.9%

Interactions

2021-03-23T08:55:37.674687image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:37.861555image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:38.044237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:38.221817image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:38.412484image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:38.596619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:38.770426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:38.951404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:39.136442image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:39.319985image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:39.500461image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:39.680645image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:39.859355image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:40.037459image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:40.224383image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:40.404458image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:40.578837image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:40.762479image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:40.947956image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:41.133209image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:41.305994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:41.473320image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:41.644480image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:41.802310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:41.970338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:42.132919image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:42.291597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:42.451456image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:42.617170image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:42.781858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:42.945518image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:43.117992image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:43.290981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:43.456454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:43.629667image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:43.794466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:43.954386image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:44.120094image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:44.289653image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:44.459933image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:44.629665image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:44.808430image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:44.984815image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:45.151138image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:45.327556image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:45.498664image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:45.664489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:45.834808image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:46.008800image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:46.185763image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:46.355003image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:46.515172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:46.676007image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:46.831843image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:46.986363image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:47.154917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:47.310646image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:47.471296image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:47.636196image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:47.799592image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:47.964581image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:48.129010image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:48.293634image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:48.452974image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:48.612224image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:48.782638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:48.938411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:49.103316image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:49.270391image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:49.437793image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:49.600228image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:49.770906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:49.937637image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:50.098169image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:50.258368image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:50.430149image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:50.597544image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:50.761297image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:50.928558image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:51.095923image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:51.259764image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:51.428427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:51.595254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:51.754916image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:51.915553image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:52.087509image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:52.257388image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:52.417912image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:52.586945image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:52.754286image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:52.917571image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:53.086213image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:53.257601image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:53.420214image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:53.582727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:53.763469image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:53.931026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:54.093271image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:54.261069image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:54.430314image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:54.596592image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:54.764741image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:54.933939image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:55.098409image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:55.262320image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:55.435758image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:55.601542image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:55.765233image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:55.933541image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:55:56.102956image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-03-23T08:56:05.242142image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-03-23T08:56:05.424426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-03-23T08:56:05.604130image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-03-23T08:56:05.796399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-03-23T08:56:05.988982image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-03-23T08:55:56.786396image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-03-23T08:55:58.068352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

amountorg:resourcedismissalconcept:namevehicleClasstotalPaymentAmountlifecycle:transitiontime:timestamparticlepointscase:concept:nameexpensenotificationTypelastSentpaymentAmountmatricolatimesincemidnightmonthweekdayhourtimesincelasteventtimesincecasestartevent_nropen_caseslabel
035.0561.0NILCreate FineA0.0complete2006-08-01 22:00:00+00:00157.00.0A1000.0missingmissing0.00.01320.08.01.022.00.00.01.011267.0deviant
135.0561.0NILSend FineA0.0complete2006-12-11 23:00:00+00:00157.00.0A10011.0missingmissing0.00.01380.012.00.023.0190140.0190140.02.012351.0deviant
235.0561.0NILInsert Fine NotificationA0.0complete2007-01-14 23:00:00+00:00157.00.0A10011.0PP0.00.01380.01.06.023.048960.0239100.03.012192.0deviant
371.5561.0NILAdd penaltyA0.0complete2007-03-15 23:00:00+00:00157.00.0A10011.0PP0.00.01380.03.03.023.086400.0325500.04.07762.0deviant
436.0561.0NILCreate FineA0.0complete2007-03-08 23:00:00+00:00157.00.0A100000.0missingmissing0.00.01380.03.03.023.00.00.01.07765.0regular
536.0561.0NILSend FineA0.0complete2007-07-16 22:00:00+00:00157.00.0A1000013.0missingmissing0.00.01320.07.00.022.0187140.0187140.02.09356.0regular
636.0561.0NILInsert Fine NotificationA0.0complete2007-08-01 22:00:00+00:00157.00.0A1000013.0PP0.00.01320.08.02.022.023040.0210180.03.09859.0regular
774.0561.0NILAdd penaltyA0.0complete2007-09-30 22:00:00+00:00157.00.0A1000013.0PP0.00.01320.09.06.022.086400.0296580.04.010882.0regular
874.0561.0NILPaymentA87.0complete2008-09-08 22:00:00+00:00157.00.0A1000013.0PP87.00.01320.09.00.022.0495360.0791940.05.014487.0regular
936.0537.0NILCreate FineA0.0complete2007-03-18 23:00:00+00:00157.00.0A100010.0missingmissing0.00.01380.03.06.023.00.00.01.07824.0regular

Last rows

amountorg:resourcedismissalconcept:namevehicleClasstotalPaymentAmountlifecycle:transitiontime:timestamparticlepointscase:concept:nameexpensenotificationTypelastSentpaymentAmountmatricolatimesincemidnightmonthweekdayhourtimesincelasteventtimesincecasestartevent_nropen_caseslabel
460546131.025.0NILInsert Fine NotificationM0.0complete2002-11-03 23:00:00+00:00142.00.0V999715.16PN0.00.01380.011.06.023.014460.083580.03.012329.0deviant
460547262.025.0NILAdd penaltyM0.0complete2003-01-02 23:00:00+00:00142.00.0V999715.16PN0.00.01380.01.03.023.086400.0169980.04.012326.0deviant
460548131.025.0NILCreate FineA0.0complete2002-09-06 22:00:00+00:00142.00.0V99980.00missingmissing0.00.01320.09.04.022.00.00.01.012132.0deviant
460549131.025.0NILSend FineA0.0complete2002-10-24 22:00:00+00:00142.00.0V999810.00missingmissing0.00.01320.010.03.022.069120.069120.02.012308.0deviant
460550131.025.0NILInsert Fine NotificationA0.0complete2002-10-30 23:00:00+00:00142.00.0V999810.00PN0.00.01380.010.02.023.08700.077820.03.012342.0deviant
460551262.025.0NILAdd penaltyA0.0complete2002-12-29 23:00:00+00:00142.00.0V999810.00PN0.00.01380.012.06.023.086400.0164220.04.012324.0deviant
460552131.025.0NILCreate FineA0.0complete2002-09-06 22:00:00+00:00142.00.0V99990.00missingmissing0.00.01320.09.04.022.00.00.01.012132.0deviant
460553131.025.0NILSend FineA0.0complete2002-10-24 22:00:00+00:00142.00.0V999915.16missingmissing0.00.01320.010.03.022.069120.069120.02.012308.0deviant
460554131.025.0NILInsert Fine NotificationA0.0complete2002-11-03 23:00:00+00:00142.00.0V999915.16PN0.00.01380.011.06.023.014460.083580.03.012329.0deviant
460555262.025.0NILAdd penaltyA0.0complete2003-01-02 23:00:00+00:00142.00.0V999915.16PN0.00.01380.01.03.023.086400.0169980.04.012326.0deviant